Publications
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Filters: Author is Hartwig Anzt [Clear All Filters]
Efficiency of General Krylov Methods on GPUs – An Experimental Study,”
2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 683-691, May 2016.
DOI: 10.1109/IPDPSW.2016.45
“Weighted Block-Asynchronous Relaxation for GPU-Accelerated Systems,”
SIAM Journal on Computing (submitted), March 2012.
(811.01 KB)
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Variable-Size Batched LU for Small Matrices and Its Integration into Block-Jacobi Preconditioning,”
46th International Conference on Parallel Processing (ICPP), Bristol, United Kingdom, IEEE, August 2017.
DOI: 10.1109/ICPP.2017.18
“Towards Continuous Benchmarking,”
Platform for Advanced Scientific Computing Conference (PASC 2019), Zurich, Switzerland, ACM Press, June 2019.
DOI: 10.1145/3324989.3325719
(1.51 MB)
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Acceleration of GPU-based Krylov solvers via Data Transfer Reduction,”
International Journal of High Performance Computing Applications, 2015.
“Evaluating the Performance of NVIDIA’s A100 Ampere GPU for Sparse and Batched Computations,”
2020 IEEE/ACM Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS): IEEE, November 2020.
(1.9 MB)
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Experiences in Autotuning Matrix Multiplication for Energy Minimization on GPUs,”
Concurrency and Computation: Practice and Experience, vol. 27, issue 17, pp. 5096 - 5113, Oct 12, 2015.
DOI: 10.1002/cpe.3516
(1.99 MB)
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